Linear regression and linear discriminant analysis are very different. Linear regression realtes a dependent variable to a set of independent predictor variables. The idea is to find a function linear in the parameters that best fits the data. It does not even have to be linear in the covariates. Linear discriminant analysis on the other hand is a procedure for classifying objects into categories. For the two-class problem it seeks to find the best separating hyperplane for dividing the groups into two catgories. Here best means that it minimizes a loss function that is a linear combination of the error rates. For three or more groups it finds the best set of hyperplanes (k-1 for the k class problem). In discriminant analysis the hypoerplanes are linear in the feature variables.

The main similarity between the two is term linear in the titles.

Linear regression and linear discriminant analysis are very different. Linear regression relates a dependent variable to a set of independent predictor variables. The idea is to find a function linear in the parameters that best fits the data. It does not even have to be linear in the covariates. Linear discriminant analysis on the other hand is a procedure for classifying objects into categories. For the two-class problem it seeks to find the best separating hyperplane for dividing the groups into two catgories. Here best means that it minimizes a loss function that is a linear combination of the error rates. For three or more groups it finds the best set of hyperplanes (k-1 for the k class problem). In discriminant analysis the hypoerplanes are linear in the feature variables.

The main similarity between the two is term linear in the titles.